Lightweight full 360 audio source location detection with two microphones

ABSTRACT

A system is described herein. The system includes at least one hardware processor that is configured to identify a pre-determined acoustic barrier filter, wherein the acoustic barrier filter coincides with the physical acoustic barrier and receive an audio signal within a time window at the first microphone and the second microphone. The hardware processor is also configured to calculate a first measure of variability, a second measure of variability, a third measure of variability, and a fourth measure of variability. The hardware processor further concatenates the first measure of variability, the second measure of variability, the third measure of variability, and the fourth measure of variability to form a feature vector, and inputs the feature vector into a location classifier to obtain an audio source location.

BACKGROUND ART

Determining a spatial location of an audio source has many applications.For example, in a smart environment or an intelligent transportationdevice, knowledge of the location of an audio source is the foundationof determining if the sound comes from an intended user, from someinterference, or from some additional source that can be used forcontext awareness. The determination of the spatial location of theaudio source also enables the use of audio enhancement techniques on theselected audio source for automatic speech recognition (ASR), speakeridentification, audio event detection, or even collision avoidance.Typically, real-time audio location requires multiple microphone arraysor sophisticated signal processing and machine learning techniques.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of amplitude and frequency content differencesas heard by a human;

FIG. 2 is an illustration of amplitude and frequency content differencesin audio as received by an electronic device;

FIG. 3 is a block diagram of feature extraction according to the presenttechniques;

FIG. 4 is an illustration of location classification;

FIG. 5 is an illustration of exemplary form factors;

FIG. 6 is an illustration of an exemplary environment where an audiosource may be located;

FIG. 7 is a process flow diagram of a method;

FIG. 8 is a block diagram of an electronic device that enables alightweight full three-hundred- and sixty-degree audio sound locationwith two microphones; and

FIG. 9 is a block diagram showing a medium that contains enables alightweight full three-hundred- and sixty-degree audio sound locationwith two microphones.

The same numbers are used throughout the disclosure and the figures toreference like components and features. Numbers in the 100 series referto features originally found in FIG. 1; numbers in the 200 series referto features originally found in FIG. 2; and so on.

DESCRIPTION OF THE EMBODIMENTS

Traditionally, high-quality, real-time audio location determinationrequires multiple microphone arrays or sophisticated signal processingand machine learning techniques. Multiple microphone arrays requireadditional power. Additionally, sophisticated signal processing andmachine learning techniques consume additional power when processing theaudio signals. Moreover, including the additional hardware and softwareto realize audio source location detection can increase an overall costof a device.

The present techniques enable a determination of an audio sourcelocation with two microphones. The audio source location may bedetermined in a full 360° degrees surrounding the two microphones. Inparticular, the present techniques include identifying a pre-determinedacoustic barrier filter, wherein the acoustic barrier filter coincideswith the physical acoustic barrier and receiving an audio signal withina time window at the first microphone and the second microphone. A firstmeasure of variability, a second measure of variability, a third measureof variability, and a fourth measure of variability may be calculatedbased on the received audio signals. The first measure of variability,the second measure of variability, the third measure of variability, andthe fourth measure of variability are concatenated to form a featurevector. The feature vector is input to a location classifier to obtainan audio source location. Thus, the present techniques enable detectingthe spatial location of a sound source captured by a two-microphonearray, with very low computer overhead.

In embodiments, the present techniques mimic the way human ears detectsound source location using only a pair of “sensors,” wherein the twomicrophones mimic the functionality of human ears. In particular, thepresent techniques enable the detection of a 360° angle of arrival usingonly a pair of microphones installed in a device (laptop, smart speaker,infotainment center, autonomous vehicle, etc.), and an acoustic barrier.The measure of variability may be a Root Mean Square (RMS) value. Inembodiments, the RMS value of a difference of unfiltered and filteredmicrophone signals may be used as a descriptor feature, and machinelearning may take as input the descriptor and estimate the location ofthe sound source based on the descriptor. In embodiments, the machinelearning technique used herein is a shallow neural network (NN)implemented as a location estimator.

In embodiments, the location of the sound source may be an angle ofarrival that is estimated or determined according to the presenttechniques. The present techniques may be implemented via low costhardware with a low computer overhead, simultaneously. In this manner,the present techniques do not require a tradeoff between hardware andsoftware, as each component is low cost and consumes a low overhead. Inparticular, the present techniques are enabled using two microphones(as, for example, most laptops already have), a small acoustic barrier(that can be already a part of the form factor), and a very lightweightalgorithm (does not require the computation of FFT or other types ofcomplex signal processing routines). The present techniques do notrequire digital signal processing (DSP) modules or dedicated hardwareacceleration. Similar to human hearing, the present techniques candetect full 360° source location. Moreover, the present techniques arenot affected by a situation in which each microphone has a slightlydifferent gain.

FIG. 1 is an illustration of amplitude and frequency content differencesas heard by a human 100. As illustrated, a sound source 102 may bepositioned substantially in front of the human 100. A sound source 104may be positioned substantially behind the human 100. As used here in,substantially in front of human 100 may refer to a position that isvisible to the human as seen through the eyes of the human. By contrast,substantially behind the human 100 may refer to a position that is notvisible to the human through the eyes of the human. In examples, audiofrom a sound source that is positioned substantially in front of thehuman will encounter different physical barriers of the human ear assound waves travel to the human eardrum when compared to a sound sourcethat is positioned substantially behind the human. In particular,components of the human ear may act as an acoustic barrier. For example,outer ear components serve to filter components of the audio frequencycomponents according to the angle of arrival of the audio. Inparticular, the audio may be filtered differently by physical outer earcomponents based on the direction from which the sound arrives. Thisdirection may indicate a location of the sound source.

Accordingly, graph 106 represents a perceived spectrum of audio contentas received from the front sound source 102. The perceived spectrum isillustrated according to a frequency content of the perceived spectrum.Note that audio received from the front sound source 102 is receivedwith a full spectrum of audio content. By contrast, the graph 106represents a perceived spectrum of audio content as received from theback-sound source 104. The perceived spectrum is illustrated accordingto a frequency content of the perceived spectrum. Note that in theexample of FIG. 1, the front sound source 102 and the back-sound source104 emit the same audio content as illustrated by the perceivedspectrums 110A and 110B. However, as illustrated at 112, the actualreceived spectrum under the solid line in graph 108 experiencesincreased filtering as the frequency of the audio content increases.

FIG. 1 illustrates a real-world scenario where a human brain uses adifference in frequency content to estimate a location of the soundsource. As used herein, a sound source refers to an entity that emanatessound. The location of the sound source may be described as a positionin space relative to an entity who hears or captures the sound, such asa human or a microphone. As illustrated in FIG. 1, humans and many otheranimals are able to estimate all-around sound source location using onlytwo ears, or “sensors.” This is possible because the shape of the earsand the presence of the head “filter out” some of the audio frequencycontent (specifically, high frequencies) in some audio directions. Thebrain uses this frequency content difference to correctly estimate soundlocation.

A determination of the location of the sound source may be used todetermine if the sound comes from an intended user, from someinterference, or some additional source that can be used for contextawareness. Moreover, in smart home environments, office environments, orintelligent transportation devices (autonomous cars, drones, etc.), thereal-time detection spatial location of audio sources can be animportant feature, which can be used to determine if the audio comesfrom the intended user or users, from some interference, or someadditional audio source that can be used for context awareness. It alsoenables the use of different types of audio enhancement techniques onthe selected audio source for ASR, speaker ID, audio event detection oreven collision avoidance.

Traditionally, high quality sound location detection is made via audiocaptured by microphone arrays, typically of around 4 to 8 elements, toallow proper location in all directions. The rationale behind this is tohave a sensor or other audio capture device in the platform generallyaimed towards any possible audio source location. This traditionaltechnique comes with the additional cost of not only multiplemicrophones, but also the processing audio channels in the platform,which can take a heavy toll on computer overhead. Such implementationsmay also require a dedicated DSP hardware.

FIG. 2 is an illustration of amplitude and frequency content differencesin audio as received by an electronic device. As illustrated in FIG. 2,the electronic device 212 may be a laptop. The electronic device 212 mayinclude a microphone array 210. The microphone array according to thepresent techniques includes two microphones. As illustrated, a soundsource 202 may be positioned substantially in front of the laptop 212. Asound source 204 may be positioned substantially behind the laptop 212.As used herein, substantially in front of the laptop 212 may refer to aposition that in front of a plane created by the lid or display screenof the laptop 212. By contrast, substantially behind the laptop 212 mayrefer to a position that behind the plane created by the lid or displayscreen of the laptop 212, wherein the speakers face the front of theplane created by the lid or display screen of the laptop 212. Inexamples, audio from a sound source that is positioned substantially infront of the laptop 212 will encounter different physical barrierscreated by the laptop when the audio travels to the microphones 210 whencompared to the sound source that is positioned substantially behind thelaptop 212. Accordingly, components of the laptop may at as an acousticbarrier. For example, the display and lid combination serve to filtercomponents of the audio content received from various directions. Inparticular, the filter may vary based on the spatial location of thelaptop. Thus, audio may be filtered differently by laptop componentsbased on the direction from which the sound arrives. This direction mayindicate a location of the sound source.

The physical acoustic barrier as described herein may be a surface thatalters a frequency component of an audio signal from an audio source.Sound that encounters the acoustic barrier may be reflected off of thesurface of the acoustic barrier. Additionally, sound may be absorbed bythe acoustic barrier and/or transmitted through the acoustic barrier.Typically, the acoustic barrier is formed from a solid material and iswide or large enough to cause a measurable effect on the frequencycontent of an audio signal. The acoustic barrier has a frequencyresponse that alters the frequency spectrum of audio signals thatencounter the acoustic barrier. As used herein, audio signals willencounter the acoustic barrier when the waveforms that comprise theaudio signals are reflected, transmitted, or absorbed by the acousticbarrier. In embodiments, an audio signal that encounters the acousticbarrier at a given frequency will respond to the encounter or collisionwith the acoustic barrier with a same change in frequency as dictated bythe frequency response of the acoustic barrier. The frequency responseof the acoustic barrier may be determined and used to derive a digitalfilter. The digital filter mimics the physical frequency response of theacoustic barrier.

The frequency response as applied to an audio signal may act as a lowpass filter. In particular, when the audio signal encounters theacoustic barrier the effect on the audio signal is to pass frequencieslower than a selected cut off frequency and attenuate signals withfrequencies that are higher than the cutoff frequency. The particularcut off frequency associated with the acoustic barrier is dependent uponthe materials used to make the acoustic barrier, the shape of theacoustic barrier, as well as other physical attributes of the acousticbarrier. In embodiments, the acoustic barrier may be designed accordingto a predetermined cut off frequency that can be used to distinguishsounds that arrive from sound source is located in front of themicrophone when compared to the predetermined cut off frequency ofsounds that arrive from a sound source that is located behind themicrophone. For example, an audio signal that must cross the acousticbarrier may experience higher filtering when compared to an audio signalthat does not cross the acoustic barrier. In this example, the audiosignal that must cross the acoustic barrier may experience morereflection of the audio signal and thus a reduced frequency content isavailable for capture by the microphones. The audio signal that does notcross the acoustic barrier may experience less reflection of the audiosignal, and thus retain more frequency content for capture by themicrophones.

In embodiments, the acoustic barrier may be designed such that certainphonemes are likely to be filtered based on the relative frequency ofthe phoneme. The relative frequency of the phoneme is the frequency ofthe phoneme as compared to other phonemes spoken by the same user. Forexample, the /s/ sound from a user may be spoken at a higher frequencyrelative to other portions of the same user's speech. Thus, the acousticbarrier may be designed with a corresponding cutoff frequency thatfilters phonemes that naturally include a higher frequency content whencompared to other phonemes.

Accordingly, graph 206 represents a perceived spectrum of audio contentas received from the front sound source 202. The perceived spectrum isillustrated according to a frequency content of the perceived spectrum.Note that audio received from the front sound source 202 is receivedwith a full spectrum of audio content. By contrast, the graph 208represents a perceived spectrum of audio content as received from theback-sound source 204. The perceived spectrum is illustrated accordingto a frequency content of the perceived spectrum. Note that in theexample of FIG. 2, the front sound source 202 in the back-sound source204 emit the same audio content as illustrated by the spectrums 214A and214B. However, as illustrated at 216, the actual received spectrum underthe solid line of graph 208 experiences increased filtering as thefrequency of the audio content increases.

The present techniques enable a location detection routine that does notrequire a spectral representation or any other numeric transformation,which enables an improvement in processing overhead. In particular, thepresent techniques enable a full 360° location detection in rooms ofdifferent sizes and shapes, with simplified hardware (two microphonearray and an acoustic barrier). In a traditional laptop with amicrophone array mounted on top of the lid, the difference of frequencycontent between the audio captured from frontal and posterior audiosources can also be used to detect such source location, using theacoustic barrier filter.

An analogous situation can be seen in other platforms, like regularlaptops, in which the couple of microphones is located in a certainorientation in which a difference of frequency content can also be usedto detect such source location. For example, in a regular laptop with atwo-microphone array mounted on top of the lid, the lid itself can beconsidered an acoustic barrier that is acoustically transparent for lowfrequency sounds, but acoustically opaque for high frequency sounds(analogously to the human ears). The frequency band in which the barrieris opaque can be modelled as an acoustic barrier filter, which is“produced” by the materials of the laptop lid itself. A scheme of thisphenomenon can be seen in FIG. 2.

FIG. 3 is a block diagram of feature extraction 300 according to thepresent techniques. In FIG. 3, the feature extraction 300 is based oncalculating the root mean square (RMS) of the difference of thenormalized time domain signals from a microphone pair. The RMS value ofa signal may represent an average power or strength associated with thesignal. In embodiments, the audio signal received by a microphoneaccording to the present techniques may be defined by a time frame orwindow. The time frame or window may be a period of time, of any length,where audio signals are captured. In embodiments, the same time windowof signal is obtained from each microphone pair. As described herein, afirst microphone of the microphone pair may be referred to as microphone1, a second microphone of the microphone pair may be referred to asmicrophone 2. The descriptors as described herein may be calculated on aper-window basis for the microphone pair. As used herein, a descriptorprovides a representation of the audio signal during the time window.

At block 302, an audio signal during an identified time window isobtained from each microphone. The audio may be digitized by capturingthe air vibrations of the sound and turning the vibrations into anelectrical signal. The air vibrations may be sampled at equaled spacedmoments in time during the time window. The sampled audio may berepresented as time vectors.

In embodiments, each microphone detects changes in air pressure andtransmits a corresponding voltage change based on the change in airpressure to an analog-to-digital converter where the voltage isperiodically sampled according to an audio sampling rate. The sampledaudio values may be a time-domain signal referred to as a time vector.At block 302, audio captured by each of microphone 1 and microphone 2 isconverted into time vectors, with a first time vector that correspondsto microphone 1 and a second time vector that corresponds to microphone2. Each time vector is normalized to eliminate the effect of eachmicrophone having a slightly different gain. The normalized time vectorfrom a first microphone is subtracted from a normalized time vector fromthe second microphone to obtain a difference in frequency contentbetween the microphone pair for the time window. In embodiments, thesubtraction is vector subtraction that is done element by element, foreach element of the time vectors. A first RMS value of the resultingdifference related to the delay between both microphone signals iscalculated to obtain a first feature coefficient. The first featurecoefficient is the RMS value of the direct difference in content betweenthe first microphone and the second microphone.

In embodiments, the RMS value may be calculated as the square root ofthe arithmetic mean of the squares of the elements in the resultingdifference in frequency content. In embodiments, the RMS value may alsobe calculated as the square of the function that defines the continuouswaveform. The calculations performed when calculating the RMS value donot include transformations such as a Fast Fourier Transform, LaplaceTransform, and the like. Thus, the use of the RMS transform results in alower computational cost when determining a location of a sound source.Moreover, the present techniques result in a reduction in power consumedwhen determining a location of a sound source due to the limited numberof microphones required in additional to a lower computation costs whencompared to other microphone arrays that use FFT based cross correlationwith deep learning algorithms. Moreover, the present techniques do notrequire the use of any additional hardware, such as an optical sensor,camera, or ultrasonic sensor. Indeed, optical devices usually cannotdetect if a certain object is producing sound by itself. Besides, theimage processing off all these optical devices always implies a verylarge amount of operations. Further, ultrasonic devices are limited tosimple detection of solid surfaces that might or might not be producingsound. Ultrasonic devices do not allow to detect active sound sources inparticular.

For ease of description, RMS values are used to derive a number offeature coefficients. However, any value that is proportional to theamplitude or energy of the signal may be used. For example, a meanabsolute value (MAV) may be applied to the difference in frequencycontent to determine a feature coefficient. Moreover, the RMS values maybe calculated in parallel.

At block 304, a second descriptor is calculated from audio capturedduring the time window for the microphone pair. At block 304, a delay isapplied to the audio signal captured by the second microphone of themicrophone pair. In embodiments, the samples captured by the secondmicrophone may be delayed by a predetermined number of samples. At block304 the second channel is delayed by a small and fixed amount of “D”samples (˜2 for a sample frequency of 16 kHz) before performing thesubtraction. The delay is not determined using cross-correlation. Inembodiments, the delay is selected such that the number of samplesrepresented by the delay is a small portion of the total number ofsamples in a single wavelength of the audio captured within the timewindow. The number of samples in the delay may be 2-5 samples.

Each time vector is normalized to eliminate the effect of eachmicrophone having a slightly different gain. Thus, the time vector assampled from audio captured by the first microphone is normalized, andthe time vector as sampled from the audio captured by the secondmicrophone and delayed is normalized.

The normalized time vector from the first microphone may be subtractedfrom a normalized delayed time vector from the second microphone toobtain a difference in frequency content for the time window. Inembodiments, the subtraction is vector subtraction that is done elementby element, for each element of the time vectors. A second RMS value ofthe resulting difference related to the delay between both microphonesignals is calculated to obtain a second feature coefficient. The secondfeature coefficient is the RMS value of a delayed difference in contentbetween the first microphone and the second microphone.

At block 306, the audio signal during the identified time window isobtained from each microphone. At block 306, an acoustic barrier filteris applied to audio captured by each of microphone 1 and microphone 2.In embodiments, the filter may be a bandpass filter coinciding with theacoustic barrier filter. This ensures that this signal will have a verydifferent profile is it is located behind the barrier, than if it is infront of it. In particular, the digital filter may emulate the frequencyresponse of the physical acoustic barrier present on the device. Thesignal from both vectors is normalized and the signals are subtractedelement by element. Then, the RMS value of the resulting subtraction iscalculated.

The present techniques enable a measure of variability, such as the RMSvalue, that distinguishes a difference between captured microphonesignals based on the location of the sound source. For example, if thesound source is located generally in front of the microphone array,without an acoustic barrier substantially impeding the path from thesound source to the microphone array, a comparison of the digitallyfiltered and unfiltered audio signals reveals very different audiosignals. In the event that the sound source is located generally inbehind of the microphone array, with an acoustic barrier impeding thepath from the sound source to the microphone array, a comparison of thedigitally filtered and unfiltered audio signals reveals similar audiosignals. In embodiments, the higher the affect of the physical acousticbarrier on an audio signal, the higher the likelihood that the audiosource is located at a position where the audio signal is significantlyimpeded by the acoustic barrier. In this scenario, the filtered andunfiltered audio signals are similar in content. However, if the audiosignal originates from a sound source substantially in front of thephysical acoustic barrier, the filtered and unfiltered audio signals aredifferent in content, as the unfiltered signal will typically contain alarger range of frequency content when compared to the digitallyfiltered signals. Thus, in embodiments a high pass filter with a samecutoff frequency as the acoustic barrier may be implemented to emphasizethe difference between audio signals from the front of the physicalacoustic barrier and the back of the physical acoustic barrier.

Accordingly, at block 306, the filtered audio signals are converted intotime vectors, with a first time vector that corresponds to microphone 1and a second time vector that corresponds to microphone 2. Each timevector resulting from the filtered audio is normalized to eliminate theeffect of each microphone having a slightly different gain. Thenormalized time vector from a first microphone is subtracted from anormalized time vector from the second microphone to obtain a differencein frequency content between the microphone pair for the time window. Inembodiments, the subtraction is vector subtraction that is done elementby element, for each element of the time vectors. A third RMS value ofthe difference related to the delay between both microphone signals iscalculated to obtain a third feature coefficient. The third featurecoefficient is the RMS value of the filtered difference in contentbetween the first microphone and the second microphone.

At block 308, a fourth feature coefficient is calculated from filteredaudio captured during the time window for the microphone pair. At block308, a delay is applied to the filtered audio signal captured by thesecond microphone of the microphone pair. In embodiments, the samplescaptured by the second microphone may be delayed by a predeterminednumber of samples. At block 308, the second channel is delayed by asmall and fixed amount of “D” samples (˜2 for a sample frequency of 16kHz) before performing the subtraction. Each time vector is normalizedto eliminate the effect of each microphone having a slightly differentgain. Thus, the time vector as sampled from audio captured by the firstmicrophone is normalized, and the time vector as sampled from the audiocaptured by the second microphone and delayed is normalized. A fourthRMS value of the resulting difference related to the delay between bothmicrophone signals is calculated to obtain a fourth feature coefficient.The fourth feature coefficient is the RMS value of a filtered anddelayed difference in content between the first microphone and thesecond microphone.

At block 310, all feature coefficients are concatenated into a finalfeature vector that corresponds to the analyzed time window. Inparticular, the first, second, third, and fourth feature coefficientsare concatenated to form a feature vector that represents the timewindow. This full feature vector includes the RMS values of the direct,delayed, filtered, and filtered and delayed channel difference found atblocks 302, 304, 306, and 308. In embodiments, the feature vector isinput to a trained neural network. The neural network may be trained todetermine a location of an audio source that output the audio capturedduring the time window.

The diagram of FIG. 3 is not intended to indicate that the examplefeature extraction 300 is to include all of the components shown in FIG.3. Rather, the example example feature extraction 300 can be implementedusing fewer or additional components not illustrated in FIG. 3 (e.g.,additional measures of variability, neural networks, filters, etc.).

FIG. 4 is an illustration of location classification 400. In FIG. 4, ascheme of the full source location detection pipeline is illustrated.FIG. 4 includes a sound source 402. A laptop 404 includes a microphonearray 406 with two microphone sensors. In particular, the microphonearray 406 includes a first microphone 406A and a second microphone 406B.The microphone 406A and 406B may capture audio signals as emitted fromthe sound source 402. Moreover, the lid of the laptop 406 serves as anacoustic barrier to the sound emitted via audio signals from the soundsource 402.

The audio signals from the sound source 402 may be processed asdescribed with respect to FIG. 3 to obtain a feature vector 408. Thefeature vector 408 may be input to a location classifier 410. Theclassifier may be, for example, a supervised machine learning classifierthat outputs a source location 412. The source location may be an anglethat identifies the location of the sound source relative to themicrophone array. For example, the location classifier may output anangle of arrival associated with the sound or an azimuth. The classifiermay be a feed forward network with two layers. The location classifier420 may be built with a shallow neural net and produces a location frominput features. The location classifier may also be able to estimatelocation in general, such as distance or elevation.

FIG. 5 is an illustration of exemplary form factors. In particular, FIG.5 illustrates examples of acoustic barriers that are implemented intwo-microphone arrays in a laptop 502, a smart speaker 508, and a smartvehicle 514. The laptop 502 may include a microphone array 504. Themicrophone array 504 includes microphones 504 a and 504B. Asillustrated, and acoustic barrier is formed by the lid 506 of the laptop502. In this manner, sound encountered by the microphone 504A and 504Bexperience filtering due to the acoustic barrier 506. The particularfiltering enabled by the acoustic barrier 506 may be used to digitallyfilter the receive signals to derive a full-length feature vector. Theparticular frequency response of the digital filtering may be the sameas the actual physical filtering provided by the acoustic barrier 506.

Smart speaker 508 may include a microphone array 510. The microphonearray 510 includes microphones 510A and 51B. Near the microphone array510, and acoustic barrier is formed. As illustrated, the acousticbarrier defines a semicircular area where the microphone 510A and themicrophone 510B are located within the semicircular area. In thismanner, sound encountered by the microphone 510A and the microphone 510Bmay experience filtering due to the acoustic barrier 512. As describedabove, the particular filtering enabled by the acoustic barrier 512 maybe used to filter the received signals to derive a full length featurevector. The particular frequency response of the digital filtering maybe the same as the actual physical filtering provided by the acousticbarrier 512.

Similarly, the vehicle 514 may include a microphone array 516. Themicrophone array 516 includes microphones 516A and 516B. Near themicrophone array 516 and acoustic barrier 518 is formed. In the exampleof the smart vehicle 514, the acoustic barrier is formed by the physicalhousing for frame of the smart vehicle 514. For example, the frame 518Aof the vehicle 514 may form a portion of the acoustic barrier.Additionally, the glass 518B positioned throughout the frame of thevehicle 514 may also form a portion of the acoustic barrier 518. Theparticular filtering enabled by the acoustic barrier 518 may be used tofilter the signals received by the microphone 516A and 516B and used toderive a full-length feature vector. The particular frequency responseof the digital filtering may be the same as the actual physicalfiltering provided by the acoustic barrier 518. While particular formfactors have been described, the present techniques may be used acrossany form factor with an acoustic barrier and two microphones. Thus, thisconcept can be implemented into different form factors, or systems, likeregular laptops, smart speakers or other home/office devices, andvehicles.

FIG. 6 is an illustration of an exemplary environment 600 where an audiosource may be located. The laptop 602 may include a microphone array604. The spherical coordinate system 606 is illustrated at one meterfrom the laptop 602, which is located at the origin of the sphericalcoordinate system 606. In embodiments, the location classifier outputs asound location as an azimuth. The azimuth may be used to determine avector from the origin to the location of the sound source. In thismanner, the location of the sound source may be identified.

Consider an exemplary use case with a total of 1500 audio segments, eachone second in duration with a 44100 Hz of sample frequency. The audiosegments may be recorded at eight different angles (0°, 45°, 90°, 135°,180°, 225°, 270°, and 315°) at a distance of one meter around the openlaptop 602. In the example of FIG. 6, the acoustic barrier filter may beselected from 4000 Hz to 8000 Hz.

In the present example, a randomly selected 80% of the segments wereused for training and the rest (20%, 300 samples) were used forvalidation. Features from the audio samples were obtained using theproposed routine described in FIG. 3, with a fixed delay D of 3 samples.A shallow fully connected neural network of 2 inputs, 2 hidden layers,and 6 neurons at the output (22 neurons in total), and with sigmoidactivation function, was trained and tested with the generated featuresdescribed in FIG. 3, and the classification results were measured andcompared with the real labels of the validation samples.

The results from the present techniques as applied to the example ofFIG. 6 are illustrated below. As can be noticed, from all 300 validationsamples, the neural network only misidentified 2 angles, which amountsfor 99.7% of correct angle of arrival classification rate.

TABLE 1 Measured Angle Real Angle 0° 45° 90° 135° 180° 235° 270° 315° 0° 42 0 0 0 0 0 2 0  45° 0 33 0 0 0 0 0 0  90° 0 0 31 0 0 0 0 0 135° 00 0 40 0 0 0 0 180° 0 0 0 0 47 0 0 0 235° 0 0 0 0 0 38 0 0 270° 0 0 0 00 0 34 0 315° 0 0 0 0 0 0 0 33

The results in Table 1 demonstrate the feasibility of implementing a twomicrophone-array with an added human-inspired acoustic barrier to detecta full 360° angle of arrival detection. The present methodology is basedon only two microphones and a very lightweight neural network technologyfor the location of a sound source, which eliminates the need of adigital signal processor (DSP) for processing incoming signals for thistask. In a very simple implementation, it successfully detects audio allaround 360° the array (which cannot be done using regular techniqueswith such a small array), with a performance of 99.3% correctclassification.

FIG. 7 is a process flow diagram of a method 700. The example method 700and be implemented in the feature extraction 300 of FIG. 3, thecomputing device 800 of FIG. 8, or the computer readable media 900 ofFIG. 9. In some examples, the method 300 can be implemented using thelocation classifier 400 of FIG. 4. At block 702, a measure ofvariability is calculated for direct differences, delayed differences,filtered direct differences, and filtered delayed differences. At block704, the calculated measures of variability are concatenated to obtain afeature vector. At block 706, the feature vector is input into alocation classifier to obtain a source location.

This process flow diagram is not intended to indicate that the blocks ofthe example method 700 are to be executed in any particular order, orthat all of the blocks are to be included in every case. Further, anynumber of additional blocks not shown may be included within the examplemethod 700, depending on the details of the specific implementation. Forexample, the audio signal may be captured by the microphone pair andnormalized prior to calculating the measure of variability.

FIG. 8 is a block diagram of an electronic device that enables alightweight full three-hundred- and sixty-degree audio sound locationwith two microphones. The location of an audio source may be determinedin real-time. The electronic device 800 may be, for example, a laptopcomputer, tablet computer, mobile phone, smart phone, a wearableheadset, a smart headset, a smart glass or speaker system, or vehicle,among others. The electronic device 800 may include a central processingunit (CPU) 802 that is configured to execute stored instructions, aswell as a memory device 804 that stores instructions that are executableby the CPU 802. The CPU may be coupled to the memory device 804 by a bus806. Additionally, the CPU 802 can be a single core processor, amulti-core processor, a computing cluster, or any number of otherconfigurations. Furthermore, the electronic device 800 may include morethan one CPU 802. The memory device 804 can include random access memory(RAM), read only memory (ROM), flash memory, or any other suitablememory systems. For example, the memory device 804 may include dynamicrandom-access memory (DRAM).

The computing device 800 may also include a graphics processing unit(GPU) 808. As shown, the CPU 802 may be coupled through the bus 806 tothe GPU 808. The GPU 808 may be configured to perform any number ofgraphics operations within the computing device 800. For example, theGPU 808 may be configured to render or manipulate graphics images,graphics frames, videos, or the like, to be displayed to a user of thecomputing device 800.

The memory device 804 can include random access memory (RAM), read onlymemory (ROM), flash memory, or any other suitable memory systems. Forexample, the memory device 804 may include dynamic random-access memory(DRAM). The memory device 804 may include device drivers 810 that areconfigured to execute the instructions for training multipleconvolutional neural networks to perform sequence independentprocessing. The device drivers 810 may be software, an applicationprogram, application code, or the like.

The CPU 802 may also be connected through the bus 806 to an input/output(I/O) device interface 812 configured to connect the computing device800 to one or more I/O devices 814. The I/O devices 814 may include, forexample, a keyboard and a pointing device, wherein the pointing devicemay include a touchpad or a touchscreen, among others. The I/O devices814 may be built-in components of the computing device 800, or may bedevices that are externally connected to the computing device 800. Insome examples, the memory 804 may be communicatively coupled to I/Odevices 814 through direct memory access (DMA).

The CPU 802 may also be linked through the bus 806 to a displayinterface 816 configured to connect the computing device 800 to adisplay device 818. The display device 818 may include a display screenthat is a built-in component of the computing device 800. The displaydevice 818 may also include a computer monitor, television, orprojector, among others, that is internal to or externally connected tothe computing device 800.

The computing device 800 also includes a storage device 820. The storagedevice 820 is a physical memory such as a hard drive, an optical drive,a thumb drive, an array of drives, a solid-state drive, or anycombinations thereof. The storage device 820 may also include remotestorage drives.

The computing device 800 may also include a network interface controller(NIC) 822. The NIC 822 may be configured to connect the computing device800 through the bus 806 to a network 824. The network 824 may be a widearea network (WAN), local area network (LAN), or the Internet, amongothers. In some examples, the device may communicate with other devicesthrough a wireless technology. For example, the device may communicatewith other devices via a wireless local area network connection. In someexamples, the device may connect and communicate with other devices viaBluetooth® or similar technology.

The electronic device 800 can also include a microphone array 826. Themicrophone array 826 includes two independent microphones. Inembodiments, each microphone may be a Micro Electrical-Mechanical System(MEMS) microphone. Audio from a sound source may be captured via themicrophone array 826. The location detector 828 may obtain theelectrical signal captured by the microphones and determine a locationof the sound source. In particular, a variability measure unit 830 maybe used to calculate feature coefficients associated with the microphonepair. In particular, the variability measure may be any value that isproportional to the amplitude or energy of the signal may be used. Forexample, an RMS value or a mean absolute value (MAV) may be applied tothe difference in frequency content to determine a feature coefficient.A concatenator 832 may concatenate multiple feature coefficients into afeature vector. A location classifier 834 may take as input the featurevector and determine a location.

The block diagram of FIG. 8 is not intended to indicate that thecomputing device 800 is to include all of the components shown in FIG.8. Rather, the computing system 800 can include fewer or additionalcomponents not illustrated in FIG. 8 (e.g., sensors, power managementintegrated circuits, additional network interfaces, etc.). The computingdevice 800 may include any number of additional components not shown inFIG. 8, depending on the details of the specific implementation.Furthermore, any of the functionalities of the CPU 802 may be partially,or entirely, implemented in hardware and/or in a processor. For example,the functionality may be implemented with an application specificintegrated circuit, in logic implemented in a processor, in logicimplemented in a specialized graphics processing unit, or in any otherdevice.

FIG. 9 is a block diagram showing a medium 900 that contains enables alightweight full three-hundred- and sixty-degree audio sound locationwith two microphones. The medium 900 may be a computer-readable medium,including a non-transitory medium that stores code that can be accessedby a processor 902 over a computer bus 904. For example, thecomputer-readable medium 900 can be volatile or non-volatile datastorage device. The medium 900 can also be a logic unit, such as anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), or an arrangement of logic gates implemented in oneor more integrated circuits, for example.

The medium 900 may include modules 906-910 configured to perform thetechniques described herein. For example, a variability measure module906 may be configured to calculate feature coefficients associated withthe microphone pair. In particular, the variability measure may be anyvalue that is proportional to the amplitude or energy of the signal maybe used. For example, an RMS value or a mean absolute value (MAV) may beapplied to the difference in frequency content to determine a featurecoefficient. A concatenate module 908 is configured to concatenatemultiple feature coefficients into a feature vector. A classificationmodule 910 may be configured to take as input the feature vector anddetermine a location. In some embodiments, the modules 906-910 may bemodules of computer code configured to direct the operations of theprocessor 902.

The block diagram of FIG. 9 is not intended to indicate that the medium900 is to include all of the components shown in FIG. 9. Further, themedium 900 may include any number of additional components not shown inFIG. 9, depending on the details of the specific implementation.

Example 1 is a system. The system includes a physical acoustic barrier;a microphone array comprising a first microphone and a secondmicrophone; at least one hardware processor that is configured to:identify a pre-determined acoustic barrier filter, wherein the acousticbarrier filter coincides with the physical acoustic barrier; receive anaudio signal within a time window at the first microphone and the secondmicrophone; calculate a first measure of variability of a directdifference of the audio signal received at the first microphone and thesecond microphone; calculate a second measure of variability of adelayed difference of the audio signal received at the first microphoneand the second microphone; calculate a third measure of variability of afiltered direct difference of the audio signal received at the firstmicrophone and the second microphone, wherein the audio signal isfiltered by the pre-determined acoustic barrier filter; calculate afourth measure of variability of a filtered delayed difference of theaudio signal received at the first microphone and the second microphone,wherein the audio signal is filtered by the pre-determined acousticbarrier filter; concatenate the first measure of variability, the secondmeasure of variability, the third measure of variability, and the fourthmeasure of variability to form a feature vector; and input the featurevector into a location classifier to obtain an audio source location.

Example 2 includes the system of example 1, including or excludingoptional features. In this example, the predetermined acoustic barrierfilter coincides with the physical acoustic barrier filter byreplicating a frequency response of the physical acoustic barrierfilter.

Example 3 includes the system of any one of examples 1 to 2, includingor excluding optional features. In this example, the location classifieris a shallow neural network.

Example 4 includes the system of any one of examples 1 to 3, includingor excluding optional features. In this example, the first, second,third and fourth measure of variability is a root mean square value.

Example 5 includes the system of any one of examples 1 to 4, includingor excluding optional features. In this example, the first, second,third and fourth measure of variability is a root mean square value.

Example 6 includes the system of any one of examples 1 to 5, includingor excluding optional features. In this example, the predeterminedacoustic barrier filter is a bandpass filter that coincides with thephysical acoustic barrier filter.

Example 7 includes the system of any one of examples 1 to 6, includingor excluding optional features. In this example, the physical acousticbarrier is a surface that alters a frequency component of the audiosignal from an audio source.

Example 8 includes the system of any one of examples 1 to 7, includingor excluding optional features. In this example, a difference iscalculated by normalizing the audio signal received by the firstmicrophone and the second microphone, and subtracting a normalized audiosignal captured by the first microphone from a normalized audio signalcaptured by the second microphone.

Example 9 includes the system of any one of examples 1 to 8, includingor excluding optional features. In this example, a delayed audio signalis generated by delaying the audio signal at the second microphone by apre-determined number of samples.

Example 10 includes the system of any one of examples 1 to 9, includingor excluding optional features. In this example, the audio sourcelocation is an angle of arrival.

Example 11 is a method. The method includes identifying a pre-determinedacoustic barrier filter, wherein the acoustic barrier filter coincideswith a physical acoustic barrier; receiving an audio signal within atime window at a first microphone and a second microphone; calculating afirst measure of variability of a direct difference of the audio signalreceived at the first microphone and the second microphone, a secondmeasure of variability of a delayed difference of the audio signalreceived at the first microphone and the second microphone, a thirdmeasure of variability of a filtered direct difference of the audiosignal received at the first microphone and the second microphone,wherein the audio signal is filtered by the pre-determined acousticbarrier filter, and a fourth measure of variability of a filtereddelayed difference of the audio signal received at the first microphoneand the second microphone, wherein the audio signal is filtered by thepre-determined acoustic barrier filter; concatenating the first measureof variability, the second measure of variability, the third measure ofvariability, and the fourth measure of variability to form a featurevector; and inputting the feature vector into a location classifier toobtain an audio source location.

Example 12 includes the method of example 11, including or excludingoptional features. In this example, the predetermined acoustic barrierfilter coincides with the physical acoustic barrier filter byreplicating a frequency response of the physical acoustic barrierfilter.

Example 13 includes the method of any one of examples 11 to 12,including or excluding optional features. In this example, the locationclassifier is a shallow neural network.

Example 14 includes the method of any one of examples 11 to 13,including or excluding optional features. In this example, the first,second, third and fourth measure of variability is a root mean squarevalue.

Example 15 includes the method of any one of examples 11 to 14,including or excluding optional features. In this example, the first,second, third and fourth measure of variability is a root mean squarevalue.

Example 16 includes the method of any one of examples 11 to 15,including or excluding optional features. In this example, thepredetermined acoustic barrier filter is a bandpass filter thatcoincides with the physical acoustic barrier filter.

Example 17 includes the method of any one of examples 11 to 16,including or excluding optional features. In this example, the physicalacoustic barrier is a surface that alters a frequency component of theaudio signal from an audio source.

Example 18 includes the method of any one of examples 11 to 17,including or excluding optional features. In this example, a differenceis calculated by normalizing the audio signal received by the firstmicrophone and the second microphone, and subtracting a normalized audiosignal captured by the first microphone from a normalized audio signalcaptured by the second microphone.

Example 19 includes the method of any one of examples 11 to 18,including or excluding optional features. In this example, a delayedaudio signal is generated by delaying the audio signal at the secondmicrophone by a pre-determined number of samples.

Example 20 includes the method of any one of examples 11 to 19,including or excluding optional features. In this example, the audiosource location is an angle of arrival.

Example 21 is at least one computer readable medium for concealingphrases in audio having instructions stored therein that. Thecomputer-readable medium includes instructions that direct the processorto identify a pre-determined acoustic barrier filter, wherein theacoustic barrier filter coincides with a physical acoustic barrier;receive an audio signal within a time window at a first microphone and asecond microphone; calculate a first measure of variability of a directdifference of the audio signal received at the first microphone and thesecond microphone, a second measure of variability of a delayeddifference of the audio signal received at the first microphone and thesecond microphone, a third measure of variability of a filtered directdifference of the audio signal received at the first microphone and thesecond microphone, wherein the audio signal is filtered by thepre-determined acoustic barrier filter, and a fourth measure ofvariability of a filtered delayed difference of the audio signalreceived at the first microphone and the second microphone, wherein theaudio signal is filtered by the pre-determined acoustic barrier filter;concatenate the first measure of variability, the second measure ofvariability, the third measure of variability, and the fourth measure ofvariability to form a feature vector; and input the feature vector intoa location classifier to obtain an audio source location.

Example 22 includes the computer-readable medium of example 21,including or excluding optional features. In this example, thepredetermined acoustic barrier filter coincides with the physicalacoustic barrier filter by replicating a frequency response of thephysical acoustic barrier filter.

Example 23 includes the computer-readable medium of any one of examples21 to 22, including or excluding optional features. In this example, thelocation classifier is a shallow neural network.

Example 24 includes the computer-readable medium of any one of examples21 to 23, including or excluding optional features. In this example, thefirst, second, third and fourth measure of variability is a root meansquare value.

Example 25 includes the computer-readable medium of any one of examples21 to 24, including or excluding optional features. In this example, thefirst, second, third and fourth measure of variability is a root meansquare value.

Some embodiments may be implemented in one or a combination of hardware,firmware, and software. Some embodiments may also be implemented asinstructions stored on the tangible, non-transitory, machine-readablemedium, which may be read and executed by a computing platform toperform the operations described. In addition, a machine-readable mediummay include any mechanism for storing or transmitting information in aform readable by a machine, e.g., a computer. For example, amachine-readable medium may include read only memory (ROM); randomaccess memory (RAM); magnetic disk storage media; optical storage media;flash memory devices; or electrical, optical, acoustical or other formof propagated signals, e.g., carrier waves, infrared signals, digitalsignals, or the interfaces that transmit and/or receive signals, amongothers.

An embodiment is an implementation or example. Reference in thespecification to “an embodiment,” “one embodiment,” “some embodiments,”“various embodiments,” or “other embodiments” means that a particularfeature, structure, or characteristic described in connection with theembodiments is included in at least some embodiments, but notnecessarily all embodiments, of the present techniques. The variousappearances of “an embodiment,” “one embodiment,” or “some embodiments”are not necessarily all referring to the same embodiments.

Not all components, features, structures, characteristics, etc.described and illustrated herein need be included in a particularembodiment or embodiments. If the specification states a component,feature, structure, or characteristic “may”, “might”, “can” or “could”be included, for example, that particular component, feature, structure,or characteristic is not required to be included. If the specificationor claim refers to “a” or “an” element, that does not mean there is onlyone of the element. If the specification or claims refer to “anadditional” element, that does not preclude there being more than one ofthe additional element.

It is to be noted that, although some embodiments have been described inreference to particular implementations, other implementations arepossible according to some embodiments. Additionally, the arrangementand/or order of circuit elements or other features illustrated in thedrawings and/or described herein need not be arranged in the particularway illustrated and described. Many other arrangements are possibleaccording to some embodiments.

In each system shown in a figure, the elements in some cases may eachhave a same reference number or a different reference number to suggestthat the elements represented could be different and/or similar.However, an element may be flexible enough to have differentimplementations and work with some or all of the systems shown ordescribed herein. The various elements shown in the figures may be thesame or different. Which one is referred to as a first element and whichis called a second element is arbitrary.

It is to be understood that specifics in the aforementioned examples maybe used anywhere in one or more embodiments. For instance, all optionalfeatures of the computing device described above may also be implementedwith respect to either of the methods or the computer-readable mediumdescribed herein. Furthermore, although flow diagrams and/or statediagrams may have been used herein to describe embodiments, thetechniques are not limited to those diagrams or to correspondingdescriptions herein. For example, flow need not move through eachillustrated box or state or in exactly the same order as illustrated anddescribed herein.

The present techniques are not restricted to the particular detailslisted herein. Indeed, those skilled in the art having the benefit ofthis disclosure will appreciate that many other variations from theforegoing description and drawings may be made within the scope of thepresent techniques. Accordingly, it is the following claims includingany amendments thereto that define the scope of the present techniques.

What is claimed is:
 1. A system, comprising: a physical acousticbarrier; a microphone array comprising a first microphone and a secondmicrophone; at least one hardware processor that is configured to:identify a pre-determined acoustic barrier filter, wherein the acousticbarrier filter coincides with the physical acoustic barrier; receive anaudio signal within a time window at the first microphone and the secondmicrophone; calculate a first measure of variability of a directdifference of the audio signal received at the first microphone and thesecond microphone; calculate a second measure of variability of adelayed difference of the audio signal received at the first microphoneand the second microphone; calculate a third measure of variability of afiltered direct difference of the audio signal received at the firstmicrophone and the second microphone, wherein the audio signal isfiltered by the pre-determined acoustic barrier filter; calculate afourth measure of variability of a filtered delayed difference of theaudio signal received at the first microphone and the second microphone,wherein the audio signal is filtered by the pre-determined acousticbarrier filter; concatenate the first measure of variability, the secondmeasure of variability, the third measure of variability, and the fourthmeasure of variability to form a feature vector; and input the featurevector into a location classifier to obtain an audio source location. 2.The system of claim 1, wherein the predetermined acoustic barrier filtercoincides with the physical acoustic barrier filter by replicating afrequency response of the physical acoustic barrier filter.
 3. Thesystem of claim 1, wherein the location classifier is a shallow neuralnetwork.
 4. The system of claim 1, wherein the first, second, third andfourth measure of variability is a root mean square value.
 5. The systemof claim 1, wherein the first, second, third and fourth measure ofvariability is a root mean square value.
 6. The system of claim 1,wherein the predetermined acoustic barrier filter is a bandpass filterthat coincides with the physical acoustic barrier filter.
 7. The systemof claim 1, wherein the physical acoustic barrier is a surface thatalters a frequency component of the audio signal from an audio source.8. The system of claim 1, wherein a difference is calculated bynormalizing the audio signal received by the first microphone and thesecond microphone, and subtracting a normalized audio signal captured bythe first microphone from a normalized audio signal captured by thesecond microphone.
 9. The system of claim 1, wherein a delayed audiosignal is generated by delaying the audio signal at the secondmicrophone by a pre-determined number of samples.
 10. The system ofclaim 1, wherein the audio source location is an angle of arrival.
 11. Amethod, comprising: identifying a pre-determined acoustic barrierfilter, wherein the acoustic barrier filter coincides with a physicalacoustic barrier; receiving an audio signal within a time window at afirst microphone and a second microphone; calculating a first measure ofvariability of a direct difference of the audio signal received at thefirst microphone and the second microphone, a second measure ofvariability of a delayed difference of the audio signal received at thefirst microphone and the second microphone, a third measure ofvariability of a filtered direct difference of the audio signal receivedat the first microphone and the second microphone, wherein the audiosignal is filtered by the pre-determined acoustic barrier filter, and afourth measure of variability of a filtered delayed difference of theaudio signal received at the first microphone and the second microphone,wherein the audio signal is filtered by the pre-determined acousticbarrier filter; concatenating the first measure of variability, thesecond measure of variability, the third measure of variability, and thefourth measure of variability to form a feature vector; and inputtingthe feature vector into a location classifier to obtain an audio sourcelocation.
 12. The method of claim 11, wherein the predetermined acousticbarrier filter coincides with the physical acoustic barrier filter byreplicating a frequency response of the physical acoustic barrierfilter.
 13. The method of claim 11, wherein the location classifier is ashallow neural network.
 14. The method of claim 11, wherein the first,second, third and fourth measure of variability is a root mean squarevalue.
 15. The method of claim 11, wherein the first, second, third andfourth measure of variability is a root mean square value.
 16. Themethod of claim 11, wherein the predetermined acoustic barrier filter isa bandpass filter that coincides with the physical acoustic barrierfilter.
 17. The method of claim 11, wherein the physical acousticbarrier is a surface that alters a frequency component of the audiosignal from an audio source.
 18. The method of claim 11, wherein adifference is calculated by normalizing the audio signal received by thefirst microphone and the second microphone, and subtracting a normalizedaudio signal captured by the first microphone from a normalized audiosignal captured by the second microphone.
 19. The method of claim 11,wherein a delayed audio signal is generated by delaying the audio signalat the second microphone by a pre-determined number of samples.
 20. Themethod of claim 11, wherein the audio source location is an angle ofarrival.
 21. At least one computer readable medium for concealingphrases in audio having instructions stored therein that, in response tobeing executed on a computing device, cause the computing device to:identify a pre-determined acoustic barrier filter, wherein the acousticbarrier filter coincides with a physical acoustic barrier; receive anaudio signal within a time window at a first microphone and a secondmicrophone; calculate a first measure of variability of a directdifference of the audio signal received at the first microphone and thesecond microphone, a second measure of variability of a delayeddifference of the audio signal received at the first microphone and thesecond microphone, a third measure of variability of a filtered directdifference of the audio signal received at the first microphone and thesecond microphone, wherein the audio signal is filtered by thepre-determined acoustic barrier filter, and a fourth measure ofvariability of a filtered delayed difference of the audio signalreceived at the first microphone and the second microphone, wherein theaudio signal is filtered by the pre-determined acoustic barrier filter;concatenate the first measure of variability, the second measure ofvariability, the third measure of variability, and the fourth measure ofvariability to form a feature vector; and input the feature vector intoa location classifier to obtain an audio source location.
 22. The atleast one computer readable medium of claim 21, wherein thepredetermined acoustic barrier filter coincides with the physicalacoustic barrier filter by replicating a frequency response of thephysical acoustic barrier filter.
 23. The at least one computer readablemedium of claim 21, wherein the location classifier is a shallow neuralnetwork.
 24. The at least one computer readable medium of claim 21,wherein the first, second, third and fourth measure of variability is aroot mean square value.
 25. The at least one computer readable medium ofclaim 21, wherein the first, second, third and fourth measure ofvariability is a root mean square value.